A method for screening microbial communities associated with radiotherapy and chemotherapy

Abstract

The present application discloses a method for screening radiotherapy and chemotherapy-related microbial communities, and screens radiotherapy and chemotherapy-related microbial communities through genomic research and analysis. The method specifically includes the following steps: the first step, sample collection and DNA extraction: the second step, 16S rRNA gene sequencing: the third step, bioinformatics analysis: analysis of pedigree relationships and identification of microbial communities. The present application can screen and determine important microbial species related to therapeutic efficacy and side effects, and observe the correlation between oral and intestinal microbiota and potential probiotic reduction, which can provide an important basis for the development of probiotic preparations to reduce the side effects of radiotherapy and chemotherapy, reduce the side effects of radiotherapy and chemotherapy, and improve survival.

Description

Technical Field

[0001] The present application relates to the technical field of beneficial bacteria and harmful bacteria related to esophageal cancer radiotherapy and chemotherapy, and in particular to a method for screening a microbial community related to radiotherapy and chemotherapy. Background Art

[0002] Esophageal cancer ranks eighth among the most common cancers and sixth among the most lethal cancers in the world. Esophageal squamous cell carcinoma (ESCC) is the main histological subtype of esophageal cancer in East Asia, while esophageal adenocarcinoma is more common in Western Europe and North America. Chinese patients account for more than half of the ESCC patients worldwide. At present, radical resection of esophageal cancer is the main treatment for non-metastatic ESCC. For patients with unresectable and locally advanced ESCC, the treatment mainly relies on radiotherapy or combined radiotherapy and chemotherapy. Due to the hidden symptoms of esophageal cancer, most patients are already in the middle and late stages when diagnosed, and direct surgery is not effective. Combined treatment methods such as surgery-based combined radiotherapy, chemotherapy, and immunotherapy have become the standard method for locally advanced esophageal cancer. The so-called "neoadjuvant therapy" is to advance the "adjuvant therapy" that was originally performed after surgery to before surgery. On the one hand, it can downstage locally advanced tumors to obtain more possibilities for radical resection. On the other hand, through effective neoadjuvant treatment methods, possible micrometastatic lesions in the body can be eliminated in order to obtain long-term survival, but the overall prognosis is poor.

[0003] Since patients often die from systemic metastases rather than local recurrences, a strategy to improve systemic control and long-term prognosis is to utilize induction chemotherapy (IC) and neoadjuvant chemoradiotherapy (nCRT). Studies have shown that nCRT combined with IC can improve pathological complete response (pCR), overall survival (OS), and disease-free survival (DFS), especially in patients with more advanced esophageal adenocarcinoma. This treatment approach has also been applied to other gastrointestinal tumors with good results. For example, a recent systematic review showed that the combination of IC+nCRT for neoadjuvant treatment of rectal cancer increased pCR by 39% compared with traditional nCRT. In addition, the use of IC may help personalized treatment, such as PET-CT scanning after IC can not only help prognosis, but also help guide the selection of chemotherapy drugs during nCRT to improve pCR rate and OS.

[0004] Chemoradiotherapy for esophageal cancer improves survival rates, thereby reducing the chances of recurrence and distant metastasis. However, this approach may result in a higher incidence of side effects, including dysphagia, dehydration, mucositis, esophagitis, dermatitis, and fatigue, all of which can adversely affect quality of life.

[0005] The intestinal flora is generally considered to be the first organ and second immune system of the human body and is closely related to the occurrence of various diseases. Modulating the microbiota provides a potential therapeutic approach and has become one of the hot topics of current research. Longitudinal evaluation of the composition of fecal flora in patients with esophageal cancer before surgery found that the intestinal flora changed significantly due to neoadjuvant cisplatin, docetaxel, and 5-fluorouracil. The combination of probiotics and prebiotics given during the neoadjuvant chemotherapy cycle may reduce these effects by restoring the composition of the intestinal flora without significantly affecting the clinical remission rate. Studies have shown that compared with the control group, patients with esophageal cancer have higher levels of bacterial diversity, with a relative abundance of Bacteroidetes and a relative lack of Firmicutes in patients with esophageal cancer; patients with esophageal cancer have lower levels of Streptococcus and higher levels of Veillonella, Porphyromonas, and Prevotella (several different types of bacteria). These results suggest that esophageal cancer has a unique microbial signature that may represent a risk factor for this condition, and if further analysis confirms these findings, they may lead to the development of innovative diagnostic and therapeutic tools.

[0006] Porphyromonas infection can reduce the effectiveness of chemotherapy and enhance the invasiveness of esophageal squamous cell carcinoma. In addition, new studies are revealing the link between oral microbiota and esophageal cancer. The abundance of actinomycetes and streptococci is significantly higher in patients with esophageal cancer. In the resected cancer tissue, the detection rate of Fusobacterium nucleatum is as high as 94.6%, Streptococcus anginosus is 67.6%, and the detection rate of other bacteria is less than 40%. In addition, the researchers evaluated the risk factors for esophageal cancer and found that esophageal cancer patients who smoked and drank alcohol had poor gum health. A prospective study found that periodontal pathogens were associated with an increased risk of esophageal adenocarcinoma, while the lack of Neisseria and Streptococcus pneumoniae was associated with a reduced risk of esophageal adenocarcinoma. In addition, bacterial carotenoid biosynthesis appears to have a protective effect against esophageal adenocarcinoma. In addition, elevated levels of the periodontal pathogen Porphyromonas gingivalis generally increase the risk of esophageal squamous cell carcinoma development. Fusobacterium nucleatum in esophageal cancer tissue is associated with a shorter survival period and may also promote tumor invasion by triggering chemokines. These findings highlight that the oral microbiota plays an important role in esophageal cancer.

[0007] However, existing studies have not found or confirmed the relationship between the oral and intestinal microbiota of esophageal cancer patients and the therapeutic effects and side effects of tumor radiotherapy and chemotherapy. Therefore, in this field, there is still a need to design methods for screening esophageal cancer radiotherapy and chemotherapy-related microbiota, and there is still a need for biomarkers that can predict the prognosis of cancer patients. Summary of the invention

[0008] In order to make up for the deficiencies in the prior art and meet the needs, the present application provides a method for screening radiotherapy and chemotherapy-related microbial communities.

[0009] This application is implemented through the following scheme:

[0010] A method for screening radiotherapy and chemotherapy-related microbial communities, comprising: obtaining in vitro samples from patients undergoing radiotherapy and chemotherapy; and screening radiotherapy and chemotherapy-related microbial communities through genomic research and analysis.

[0011] By comparing the changes in the microbiota before and after chemoradiotherapy, the microbial species associated with the efficacy and side effects of chemoradiotherapy were identified.

[0012] Preferably, the microbial community includes beneficial bacteria and harmful bacteria.

[0013] Preferably, the beneficial bacteria and harmful bacteria are microorganisms associated with esophageal cancer radiotherapy and chemotherapy.

[0014] Preferably, the microbiota includes beneficial and harmful bacteria of the oral cavity and the intestine.

[0015] More preferably, the beneficial bacteria include: Porites australis, Streptococcus pharyngeus, TM7, Lactobacillus gasseri, Ruminococcus, Bacteroides intestinalis, Lactobacillus salivarius, Psychrobacterium psychrophilum, Lactobacillus mucosa;

[0016] More preferably, the harmful bacteria include: Streptococcus pneumoniae, Capnocytophaga, Weissella, Bifidobacterium longum, and Bacteroides vulgaris.

[0017] The method specifically comprises the following steps:

[0018] Step 1: Sample collection and DNA extraction: After the patient's pathological diagnosis, stool and saliva samples were collected and stored in sterile containers at -20°C using RNAlater. 200 mg of microbial macro DNA was extracted from each sample using a trace sample DNA extraction kit.

[0019] Step 2: 16S rRNA gene sequencing: A high-fidelity PCR mixture was used for PCR reaction. Before sequencing, the barcode-specific primers 515F-806R were used to amplify different 16S rRNA genes in the highly variable region V3-V4. A DNA-free PCR sample protection kit was used to generate a sequence library from the purified PCR product. The quality of the library was determined using a fluorimeter and a bioanalyzer. Then, sequencing was performed on a sequencer to generate 250 bp double-end reads.

[0020] Step 3: Bioinformatics analysis: assign the obtained paired-end reads to samples according to the barcode; truncate the barcode and primer sequences, and connect the truncated reads using FLASH; perform quality control on the original tags, compare the clean tags with the reference database to detect chimeric sequences; obtain valid tags after removing chimeric sequences; perform sequence analysis, 97% of the sequences are distributed on the same OTU, perform sequence screening for each OTU, annotate the classification data, perform multiple sequence alignment, analyze phylogenetic relationships and identify microbial communities.

[0021] Beneficial Effects

[0022] 1. A method for screening radiotherapy and chemotherapy-related microbial communities of the present invention can screen and determine important microbial species related to therapeutic efficacy and side effects, and observe the correlation between oral and intestinal microbial communities and the reduction of potential probiotics.

[0023] 2. A method for screening radiotherapy and chemotherapy-related microbiota of the present invention can determine the effects of certain key oral and intestinal microbiota, including Parabacillus heteroschisis, Parabacillus, long chain Bacillus subgroup, Streptococcus angiopathy, etc., which are positively correlated with the treatment effect of the patient, and Streptococcus pneumoniae, Musciclosporin, TM7_phylum_sp_oral_clone_cw040, Bifidobacterium longum subspecies long, etc., which are negatively correlated with the treatment effect of the patient. These results show that radiotherapy and chemotherapy of esophageal cancer change the composition of oral and intestinal microbiota, and certain microbiota are related to the efficacy of radiotherapy and chemotherapy, which can provide an important basis for the development of probiotic preparations to reduce the side effects of radiotherapy and chemotherapy, reduce the side effects of radiotherapy and chemotherapy, and improve survival. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 To compare the oral microbiota of patients with esophageal cancer before and after treatment;

[0025] Figure 2 To compare the intestinal microbiota of patients with esophageal cancer before and after treatment;

[0026] Figure 3 To analyze the correlation between oral and intestinal microbiota in patients with esophageal cancer;

[0027] Figure 4 To analyze the correlation between oral and intestinal microbiota and clinical characteristics and treatment factors in patients with esophageal cancer. DETAILED DESCRIPTION

[0028] In this application, unless otherwise specified, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. In addition, the protein and nucleic acid chemistry, molecular biology, cell and tissue culture, microbiology, immunology related terms and laboratory operation steps used herein are terms and routine steps widely used in the corresponding fields. At the same time, in order to better understand the present invention, the preferred embodiments of the present invention are further described below in conjunction with the accompanying drawings. However, the embodiments are only for illustrative purposes and are not intended to limit the present invention.

[0029] In one embodiment, the present application provides a method for screening chemoradiotherapy-related microbial communities, which comprises: obtaining in vitro samples from patients undergoing chemoradiotherapy; and screening chemoradiotherapy-related microbial communities through genomic research and analysis.

[0030] In one embodiment, the present application determines the microbial species associated with the efficacy and side effects of chemoradiotherapy by comparing the changes in the microbiome before and after chemoradiotherapy.

[0031] Preferably, the microbial community includes beneficial bacteria and harmful bacteria.

[0032] Preferably, the beneficial bacteria and harmful bacteria are microorganisms associated with esophageal cancer radiotherapy and chemotherapy.

[0033] Preferably, the microbiota includes beneficial and harmful bacteria of the oral cavity and the intestine.

[0034] Preferably, the beneficial bacteria include: Porites australis, Streptococcus pharyngeus, TM7, Lactobacillus gasseri, Ruminococcus, Bacteroides intestinalis, Lactobacillus salivarius, Psychrobacterium psychrophilum, Lactobacillus mucosa;

[0035] Preferably, the harmful bacteria include: Streptococcus pneumoniae, Capnocytophaga, Weissella, Bifidobacterium longum, and Bacteroides vulgaris.

[0036] Specifically, the screening method comprises the following steps:

[0037] Step 1: Sample collection and DNA extraction: After the patient's pathological diagnosis, stool and saliva samples were collected and stored in sterile containers at -20°C using RNAlater. 200 mg of microbial macro DNA was extracted from each sample using a trace sample DNA extraction kit.

[0038] Step 2, 16S rRNA gene sequencing: A high-fidelity PCR mixture was used for PCR reaction. Before sequencing, the barcode-specific primers 515F-806R (commercially available) were used to amplify different 16S rRNA genes in the V3-V4 hypervariable region. A DNA-free PCR sample protection kit was used to generate a sequence library from the purified PCR product. The quality of the library was determined using a fluorimeter and a bioanalyzer. Then, sequencing was performed on a sequencer to generate 250 bp double-end reads.

[0039] Step 3: Bioinformatics analysis: assign the obtained paired-end reads to samples according to the barcode; truncate the barcode and primer sequences, and connect the truncated reads using FLASH; perform quality control on the original tags, compare the clean tags with the reference database to detect chimeric sequences; obtain valid tags after removing chimeric sequences; perform sequence analysis, 97% of the sequences are distributed on the same OTU, perform sequence screening for each OTU, annotate the classification data, perform multiple sequence alignment, analyze phylogenetic relationships and identify microbial communities.

[0040] In the embodiments, the bioanalyzer uses the Agilent Bioanalyzer 2100 system, the sequencer uses the Illumina Hi Seq 2500 platform, the original tags are quality controlled by the QIIME quality assurance process, the sequence analysis is performed by Uparse software, OTU is the abbreviation of operational taxonomic unit, the Green Gene Database in the RDP classifier algorithm is used to perform sequence screening for each OTU, and multiple sequence alignment is performed using MUSCLE software.

[0041] In this application, samples of patients were collected and DNA was extracted. The data were analyzed by bioinformatics through 16S rRNA gene sequencing. The mean ± standard deviation (SD) or median (interquartile range) was expressed according to the normality of its distribution. One-way analysis of variance, receiver operating characteristic curve (ROC) and nonparametric tests were performed using SPSS 26.0 version. Alpha diversity analysis was performed using the Wilcoxon rank sum test, including Ace, Shannon and Chao indexes. P < 0.05 was considered statistically significant between the groups.

[0042] In a specific experimental example, a stool or saliva sample is collected from a subject or patient. Usually, a saliva sample is collected immediately after the stool sample is collected.

[0043] The specific steps are as follows:

[0044] Stool sampling

[0045] (1) Choose a common stool sampling tube without preservation fluid, and obtain the sampling tube, foam box and ice pack (at least one) from the hospital when the patient visits the hospital;

[0046] (2) Store the ice pack in a -20°C refrigerator;

[0047] (3) When the patient defecates, he / she should empty the urine first and then discharge the feces into a clean paper box (a layer of clean A4 paper can be placed inside the box);

[0048] (4) Take samples using the above sampling method (keep your hands clean);

[0049] (5) Tighten the tube cap and write your name, sample type, sampling time, and number on the tube wall;

[0050] (6) Use tape to wrap the sampling tube and ice pack together, place it in a foam box, and deliver it to the hospital on the day of sampling as required; if it is not possible to deliver it to the hospital on the same day, the sampling tube must be placed in a foam box and stored in a -20℃ refrigerator.

[0051] Saliva sampling:

[0052] (1) Prepare the necessary equipment;

[0053] (2) Stick the label paper ② on the 50mL cryotube ① and the ziplock bag ③. Indicate the sampling subject information (name, sample number, sample type, sampling time, etc.) on all labels;

[0054] (3) 30 minutes before sampling, rinse out any debris in your mouth with drinking water; do not eat, drink, smoke or chew gum during these 30 minutes.

[0055] Next, DNA was extracted from the samples, and 16S rRNA gene sequencing was performed, and the data were analyzed using bioinformatics.

[0056] Microbial diversity sequencing is a research method that uses high-throughput measurement technology to detect characteristic sequences of microbial species such as 16S, 18S, and ITS amplified by PCR. This type of method does not require the separation, purification, and cultivation of microorganisms, and is particularly suitable for the study of samples such as environmental microorganisms. In this embodiment, 16S rDNA is used as the ribosomal characteristic coding sequence of prokaryotes, which is used for species classification and evolutionary relationship research of bacteria and archaea. The general process includes genomic DNA extraction, PCR amplification of the target region, recovery and quantification of amplified products, library construction, and sequencing on the machine.

[0057] In this application, the experimental process is as follows: after extracting genomic DNA from the sample, the V3+V4 region of 16S rDNA is amplified using specific primers with barcodes.

[0058] Primer sequences include:

[0059] 341F: SEQ ID NO.1

[0060] CCTACGGGNGGCWGCAG

[0061] Wherein N represents any one of A, T, G, and C, and W represents A or T;

[0062] 806R: SEQ ID NO.2

[0063] GGACTACHVGGGTATCTAAT

[0064] Wherein, V represents any one of A, G, and C, and H represents any one of A, C, and T.

[0065] Then the PCR amplification products were cut and recovered, and quantified using QuantiFluorTM fluorometer. The purified amplification products were mixed in equal amounts, connected to sequencing adapters, and sequencing libraries were constructed and sequenced on Hiseg2500 PE250.

[0066] The information analysis process is as follows: Microbial diversity research is mainly divided into five parts: diversity research, functional research, sample difference comparison, and environmental relationship research. After sequencing to obtain raw reads, we filter low-quality eads, and then assemble and filter them to ensure that the most effective data is clustered into OTUs. After obtaining OTUs, according to the analysis process, species annotation, 0 diversity analysis, B diversity analysis, PICRUSt and other community function predictions are performed in sequence. In the case of effective grouping, inter-group difference comparison and difference test are performed. Finally, combined with other factors (such as environmental factors), specific advanced analyses such as CCA are performed to answer the relationship between microorganisms and the environment.

[0067] In this embodiment, the data processing process is as follows: After the raw data is obtained by sequencing, a large amount of low-quality data or biologically meaningless data (such as chimeras) is generated due to PCR errors, sequencing errors, etc. Therefore, in order to ensure that the subsequent analysis has statistical reliability and biological validity, we will perform strict quality control in the reads utilization, TAS splicing and other multi-person data processing processes, and finally obtain effective TAS to carry out subsequent OTU clustering and other analyses. Data filtering and tag splicing: The original offline data (raw data) obtained by sequencing contains certain low-quality data, etc., which will affect the subsequent analysis, so the original offline data needs to be preprocessed. The specific processing steps are as follows: raw data filtering; tag splicing; tag filtering; tag removal of chimeras. Tags abundance statistics: The number and abundance of tags are closely related to the amount of sequencing data, sample type, etc. The statistics of tags are conducive to a preliminary judgment of the sample situation. The tag sequences were processed using the Mothur software package to remove redundancy and select unique tag sequences (this sequence is actually a group of completely identical tag sequences. Each unique tag represents a different number of tag sequences, and the number of tags represented by each unique tag is the abundance of the unique tag).

[0068] In this embodiment, the process of OTU analysis is as follows: OTU (Operational Taxonomi Units) refers to artificially set taxonomic units for the convenience of phylogenetic or population genetics research. Different tags can be clustered into OTUs using the sequence similarity relationship between effective tags. After obtaining OTUs, various core analyses such as species annotation, community diversity, and intergroup differences can be carried out one by one using relevant software based on their abundance and sequence information. OTU clustering: In order to study the compositional diversity information of the species of the samples, all the Effective Tags sequences of all the samples were clustered using Uparse software. By default, the sequences were clustered into OTUs (Operational Taxonomic Units) with 97% consistency (ldentity), and the absolute abundance and relative information of each OTU in each sample were calculated. In the process of OTUs construction, the Effective Tas data, low-frequency as data and Tas annotation data of different samples were preliminarily statistically analyzed. OTU comparative analysis: There is a certain degree of similarity and specificity in the species distribution of microbial communities in different habitats. In the case of multiple groups (or samples), in order to understand the differences in OTUs between different groups (or samples), we can conduct Venn diagram analysis based on OTU abundance information to understand the common or unique information of OTUs between different samples or groups.

[0069] In this embodiment, the specific process of species classification analysis includes the following steps: microbial species classification is generally divided into 7 levels: kingdom, phylum, class, order, family, genus, and species, and each OTU represents a certain type of classification level set. Therefore, species annotation can be performed based on the sequence information of OTU, and the analysis results can be associated with the actual biological significance, so as to study the change relationship of species in the community. Species annotation method: Uparse will select representative sequences (the Tag sequence with the highest abundance in OTUS) in the process of constructing OTUS, and use the Naive Bayesianassignment algorithm of RDPClassifier to perform species annotation with the silva database (the confidence value is set to 0.8-1). After obtaining the species annotation information of each OTU, in order to study the phylogenetic relationship between OTUs, KRONA is used to visualize the species annotation results. The circles represent different classification levels from the inside to the outside, and the size of the fan represents the relative proportion of different OTU annotation results. According to the species annotation information of OTU, the number of Tas sequences of each sample at each classification level (kingdom, phylum, class, order, family, genus, species) is counted, and a bar graph is drawn.

[0070] Combining the species annotation information of OTU and the expression of OTU in different samples, we counted the expression of each sample at each classification level according to kingdom->phylum->order->family->species. The number of tags belonging to the same species in different samples is summarized in the same table to generate a species profiling table, as shown in the following table. The first column in the table is the classification level, 0 represents the kingdom (Domain1 represents](Phyum represents (Cass, and so on. The second to eighth columns are the species classification information corresponding to the non-classification level, and the ninth to sixth columns represent the number of tag sequences contained in each sample (species absolute abundance, and the sixth to twenty-eight columns are the percentage of the number of tag sequences contained in each sample to the total number of tags (species relative abundance). From this profiling table, it is easy to compare the abundance differences of the same species in different samples, so as to find out the species with significant differences in different samples.

[0071] Using perl+SVG software, we selected some species taxonomic units with an abundance greater than 1% based on the data of the taxonomic expression spectrum of species, and made a display diagram of the species classification tree. Dominant species largely determine the ecological structure and functional structure of the microbial community. Understanding the species composition of the community at each level can effectively interpret the formation, change and ecological impact of the community structure. We counted the species composition of each sample at each level of classification, and then used a stacked chart to intuitively display the changes in species abundance of different samples at each level of classification. For the beauty of the drawing, we only show the top 10 species with an expression abundance of 2% in at least one sample. The remaining species are uniformly classified into the Other category, and tags that cannot be annotated to this level are classified into the Unclassified category.

[0072] The software used for the heatmap analysis of species classification is the pheatmap package of the R language. Based on the expression spectrum data of species classification, we use heatmaps to display the expression of different species among samples. At the same time, based on the clustering relationship on the heatmap, we can also reflect the sample relationship. In order to reduce the impact of noise data, the species selected for heatmap analysis need to meet the following two conditions: (1) the relative abundance (number of species tags / total number of tags) of the species in at least one sample reaches more than 0.1%; (2) the species with the top 25 relative total abundance that meet condition (1) are selected for heatmap analysis.

[0073] In this embodiment, the specific process of Alpha diversity analysis is as follows: Alpha diversity refers to the diversity in a specific habitat or ecosystem, which can indicate the degree to which the habitat is isolated by species. It is usually calculated using two important parameters: species richness (species) and species uniformity (distribution). The final report of this project mainly shows Chaol, ACE, Shannon, observed species, Simpson and Good's Coverage, six commonly used alpha diversity indices, and their related analysis results. In general, the Chaol / ACE index is mainly concerned with the species richness information of the sample, Good's Coverage reflects the low-abundance OTU coverage of the sample, observed species indicates the OTU species that can be detected, and Simpson / Shannon mainly comprehensively reflects the richness and uniformity of the species. For AIpha diversity index statistics, we use QIIME software to calculate the Alpha diversity index and draw the corresponding curve.

[0074] We evaluate whether the sequencing volume is sufficient to cover all taxa by drawing a rarefaction curve, and indirectly reflect the richness of species in the sample. The rarefaction curve uses the relative proportion of known OTUs in the measured sequences to calculate the expected value of the number of OTUs that appear when n (n is less than the total number of measured tag sequences) tags are extracted, and then a curve is drawn based on a set of values ​​(generally a set of arithmetic progressions less than the total number of sequences) and the expected value of the number of OTUs corresponding to them. When the curve tends to be flat or reaches a plateau, it can be considered that the sequencing depth has basically covered all species in the sample. We evaluate whether the sequencing volume is sufficient by drawing a Shannon rarefaction curve, and indirectly reflect the richness of species in the sample. The Shannon index is an indicator for evaluating the degree of species diversity in a sample, and the higher the value, the higher the degree of diversity. The Shannon dilution curve is calculated by sampling n tags to calculate the expected value of the Shannon index, and then a curve is drawn based on a set of n values ​​(generally a set of arithmetic progressions less than the total number of sequences and the corresponding Shannon expected value. When the curve tends to be flat or reaches a plateau, it can be considered that the increase in sequencing depth has no effect on species diversity and the sequencing volume tends to be saturated. We sort the OTUs in the sample from large to small according to relative abundance (or the number of sequences contained) to obtain the corresponding ranking number, and then use the ranking number of OTUs as the horizontal axis and the relative abundance in OTUs (the relative percentage of the number of sequences in the OTU of this level can also be used) as the vertical axis to connect these points with a broken line, that is, to draw the Rank Abundance curve. The Rank Abundance curve can intuitively reflect the taxonomic richness and uniformity contained in the sample, that is, in the horizontal direction, the taxonomic abundance is reflected by the width of the curve. The higher the taxonomic richness, the larger the span of the curve on the horizontal axis. The smoothness of the curve in the vertical direction reflects the uniformity of the taxonomy in the sample. The flatter the curve, the more uniform the species distribution.

[0075] Environmental drivers in different habitats can cause differences in microbial Q diversity. Combining grouping and sampling information, by conducting hypothesis tests on Q diversity between two or more groups, we can analyze whether there are significant differences in species diversity between groups, thereby preliminarily determining the potential factors driving changes in community diversity. We also use the following common hypothesis testing methods to perform difference analysis. (1) When comparing two groups, use the T-test and the Wilcox rank sum test. (2) When comparing more than two groups, use the Tukey test and the Kruskal-Wallis rank sum test.

[0076] Beta diversity is a comparison of diversity between different ecosystems. It is the rate of change of species composition along environmental gradients or between communities. It is used to express the response of biological species to environmental heterogeneity. Generally speaking, the calculation of community Beta diversity under different environmental gradients includes two parts: species change (number) and species generation (presence or absence). Therefore, we will use the Weighted Unifrac and Unweighted Unifrac indices based on these two important indicators for subsequent Beta diversity analysis.

[0077] In the study of microbial β diversity, Weighted Unifrac and Unweighted Unifrac are commonly used sample distance calculation methods. Compared with the distance calculation methods such as Jacard and Bray-Curtis, Unifrac fully considers the evolutionary relationship (base variation information) between OTU representative sequences, which is more in line with the actual biological significance of microbial community changes. In addition to the characteristics of considering the evolutionary relationship between OTUs, Unifrac can be divided into weighted (WeightedUunifrac) and unweighted (Unweichted Unifrac) methods according to whether or not the OTU abundance is considered. Among them, Unweighted UniFrac only considers the changes in the presence or absence of species, while Weighted UniFrac considers the changes in the presence or absence of species and species abundance at the same time. Therefore, in actual analysis, combining the two Unifrac analysis methods can more effectively discover the structural difference information between samples. The Pheatmap package of R language is used to display the weighted and unweighted Unifrac indexes between groups in the form of heat maps. Based on the species principal information of the OTU list, principal component analysis (PCA) can be carried out to study the composition distance relationship between samples using the idea of ​​dimensionality reduction. This method can effectively find the "important" elements and structures in the data by using variance decomposition, and reflect the complex sample composition relationship on the two eigenvalues ​​of the horizontal and vertical coordinates, thereby achieving the effect of simplifying the complexity of the data. In the analysis results, the more similar the sample composition is, the closer the distance reflected in the PCA graph is, and samples from different environments may often show their own clustered distribution.

[0078] PCoA principal coordinate analysis is an analysis method that shows the similarity between samples. Its analysis idea is basically the same as PCA analysis, both of which use dimensionality reduction to find the main sample difference distance in complex samples. Unlike PCA, PCoA mainly uses pairing information such as weighted and unweighted Unifrac, so the results are more focused on reflecting the dissimilarity distance between samples. Based on the weighted and unweighted Unifrac data results between samples, we can draw PCoA graphs. In the analysis results, the more similar the samples are, the closer the distance reflected in the PCoA graph, and samples from different environments may often show their own clustered distribution.

[0079] Figure 1 Figure 2 shows the diversity and abundance of oral microbiota in patients with esophageal cancer before (SB group) and after (SA group) treatment. After treatment, the alpha diversity of the oral microbiota decreased slightly, which is consistent with the results of other cancer studies ( Figure 1 a); β-diversity measures the similarity of microbial composition between individuals. It can be observed that there are significant differences in the oral microbiota between the SB and SA groups ( Figure 1 b). Similarly, the effects of chemoradiotherapy on the oral microbiota in patients with head and neck squamous cell carcinoma support the differences in β-diversity of the oral microbiota after chemoradiotherapy. However, studies on the β-diversity of the microbiota before and after chemoradiotherapy are still limited. Figure 1 c is the change in relative abundance. It can be seen that the relative abundance of Streptococcus, Micrococcaceae, Lactobacillaceae, Saccharomonasaceae, Gastrostreptococcaceae, and Micrococcaceae decreased after chemoradiotherapy. Lactobacillaceae is a key beneficial species in the oral microbiome, and its number decreased significantly after chemoradiotherapy, indicating that the homeostasis of the oral microbiome and oral health may be disrupted, which may lead to stomatitis in patients receiving chemoradiotherapy. As oral pathogens, the increase of Porphyromonadaceae and Prevotellaceae may be related to chronic periodontitis and other oral infections.

[0080] Figure 1 d shows a comparison of the number of species between the SA and SB groups. There was only a slight difference in the number of oral microbial species before and after treatment. The SA group had 185 species, and the SB group had 4 more species than the SA. Specifically, the SA and SB had 159 species in common, of which 26 were unique to SA and 30 were unique to SB. Studies have shown a decrease in the number of species in oropharyngeal swabs after chemoradiotherapy, which may be attributed to the adverse effects of chemoradiotherapy on the oral microbiota.

[0081] In the SA group, there was a significant increase in the Porphyromonadaceae cluster, Selenomonas_sp_oral_taxon_136, and Porphyromonas_sp_oral_clone_HF001 ( Figure 1 e). Porphyromonas endodontics is a member of the Porphyromonas family and produces lipopolysaccharide, which is a major pathogenic factor associated with the development of periapical disease and alveolar bone loss. In addition, Selenomonas is a periodontal pathogen associated with the pathogenesis of chronic periodontitis or systemic aggressive periodontitis. In contrast, the presence of pathogens Burkholderia and Leuconostoc decreased in the SA group. Leuconostoc mesenteroides LVBH107 is an enteric Leuconostoc strain, a member of the Leuconostocaceae family, and has been shown to have the ability to effectively inhibit the formation of P. gingivalis biofilm and reduce inflammation. This beneficial bacterium can reduce the production of proinflammatory cytokines and downregulate the expression of inflammation-related genes. In summary, chemoradiotherapy increased pathogenic bacteria and reduced beneficial bacteria in saliva samples.

[0082] Figure 1 f The changes in potentially harmful bacteria before and after treatment and the reduction in potentially harmful bacteria in the SB group were further analyzed. The number of TM7, Proteobacteria, Bacteroidetes, and Actinobacteria decreased in the SB group, and the number of Firmicutes increased. TM7 is a unique epiphytic microorganism that parasitizes on the surface of host bacteria and is significantly associated with dysbiosis in gingivitis and other inflammatory conditions, suggesting its pathogenic potential. However, recent studies have shown that TM7 may play a regulatory role on host bacteria. Studies in mice have shown that TM7 can reduce gingivitis and alleviate bone loss. Proteobacteria include various pathogens, including the well-known Escherichia coli. Bacteroides fragilis can dislocate and infect the human body, causing various infections. Some pathogenic mycobacteria exist in species of the genus Actinomycetes, such as Mycobacterium tuberculosis, Mycobacterium leprae, and Mycobacterium abscessus.

[0083] This example compares the oral microbiome before and after chemoradiation, focusing on abundance and diversity. Post-treatment results show a significant decrease in beneficial oral bacteria and a significant increase in harmful bacteria. This change is characterized by a decrease in Leuconostoc and Lactobacillus, while increasing Porphyromonas and Prevotella. These microbiome changes may exacerbate any pre-existing damage to the oral mucosa caused by treatment, and changes in the diversity of the oral microbiome after treatment indicate possible dysbiosis in the oral cavity.

[0084] Figure 2 Figure 2 shows the diversity and abundance of intestinal flora in patients with esophageal cancer before (FB group) and after (FA group) treatment. α-diversity did not change significantly after treatment ( Figure 2 a), while β-diversity was significantly different ( Figure 2 b). In patients with esophageal cancer, the relative abundance of the gut microbiota was also altered after chemoradiotherapy ( Figure 2c). After treatment, the abundance of Helicobacteraceae, Bifidobacteriaceae, Streptococcusaceae, Enterococcaceae, Bacteroidetes, Flavobacteriaceae, and Moraxellaceae decreased. In contrast, the abundance of Enterobacteriaceae, Ruminococcaceae, and Lactobacillaceae increased after treatment. This application compared the species counts of the intestinal flora before and after treatment and observed a decrease in the flora count after treatment. There were 183 species before treatment, which decreased to 33 species after treatment. Before treatment, there were 500 specific species; after treatment, this decreased to 17 species. SA and SB had a total of 133 species ( Figure 2 d) There is evidence that the number of species in the microbiota differs significantly before and after treatment, which means that treatment may disrupt the balance of the gut microbiota.

[0085] The relative abundance of the intestinal microbiota changed before and after treatment at both the family and species levels. At the family level, the abundance of the Cocciaceae family increased significantly, while the abundance of the Leuconostocaceae family decreased significantly in the FA group. At the species level, the abundance of Bacteroides fragilis, Lactobacillus fermentum, Akkermansia muciniphila, and Ruminococcus callosus ATCC27760 increased significantly in the FA group, while the abundance of Bacteroides vulgaris and Weissella decreased significantly in the group ( Figure 2 e). Bacteroides is an anaerobic intestinal bacterium and opportunistic pathogen that causes life-threatening infections when it migrates from its niche in the intestine to other parts of the body, and its presence is closely associated with colorectal cancer. In addition, the Ruminococcaceae family has been associated with Tourette's syndrome, and Ruminococcus is more abundant in patients with cervical spondylitis-associated Crohn's disease. Bacteroides vulgaris regulates CD82 expression and promotes NLRP3 inflammasome activation. Inhibition of CD82 can enhance NLRP3 inflammasome activation through brcc3-dependent K63 deubiquitination, thereby reducing the pathogenesis of colitis. Therefore, Bacteroides vulgaris has become a potential therapeutic target for colitis. The probiotic and antimicrobial properties of Weissella confusa have been shown to have health benefits. Weissella confusa is a member of the Weissella genus and has enhanced antimicrobial activity against several bacteria, including Escherichia coli and Listeria monocytogenes.

[0086] After treatment, the relative abundance of intestinal flora in patients was analyzed ( Figure 2 f). At the phylum level, the relative abundance of aerobic bacteria in Firmicutes and Bacteroidetes decreased, while the relative abundance of Verrucomicrobia, Proteobacteria, and Actinobacteria increased. The relative abundance of anaerobic bacteria in Bacteroidetes decreased, while the relative abundance of anaerobic bacteria in Actinobacteria and Firmicutes increased. There was no significant change in the relative abundance of Proteobacteria. These results suggest that radiochemotherapy may cause a certain degree of dysbiosis in the intestinal flora of patients.

[0087] By comparing the intestinal microbiota before and after treatment, a significant decrease in species diversity can be observed, indicating that chemoradiotherapy significantly changes the composition of the intestinal microbiota. This effect may lead to intestinal microbiota dysbiosis. At the same time, potentially beneficial intestinal microbiota, such as Bacteroides vulgaris and Escherichia coli, decreased, while potentially harmful intestinal microbiota, such as Bacteroides vulgaris and Oncococcus, increased. Studies have shown that the increase in potentially harmful microbiota may affect the development of intestinal inflammation, and further research is needed to determine whether this phenomenon will exacerbate radiation-induced inflammation.

[0088] This example further analyzes the correlation between oral and intestinal microbiota. It can be found that this is helpful to develop a combined treatment formula that synchronously regulates oral and intestinal microbiota. Figure 3 Figure 2 shows the correlation between oral and gut microbiota in patients with esophageal cancer. Some differences in β-diversity were observed at the family and species levels ( Figure 3 a, 3c). In particular, at the species level, there were clear differences between the oral and gut microbiota. There was minimal overlap between the oral and gut microbiota, and significant differences in β-diversity. Notably, after chemoradiotherapy, differences in β-diversity became more pronounced as the α abundance of the microbiota changed.

[0089] This application compares the abundance of oral and intestinal microbiota in patients with esophageal cancer before and after treatment ( Figure 3 b). At the family level, the abundance of eight families (Bacteroidetes, Enterococcus, Micrococcaceae, Bifidobacteriaceae, Lactobacillaceae, Enterobacteriaceae, Prevotellaceae, and Ruminococcaceae) increased in the FA group compared with the FB group. However, the FA changes of Lachnospiraceae and Streptococcaceae were not significant. Compared with SB, the abundance of six families decreased in SA, including Bacteroidetes, Enterococcus, Micrococcaceae, Enterobacteriaceae, Lachnospiraceae, and Streptococcaceae. Prevotellaceae and Ruminococcaceae were the two families with increased abundance in SA. The oral cavity of patients with periodontitis is rich in Prevotellaceae, which is potentially harmful to mucosal surfaces, associated with early childhood caries, and may have potential effects on systemic diseases. Although Streptococcus is generally considered a pathogen, many species in this genus are also beneficial to the oropharynx and play a role in preventing oral infections, otitis media, and streptococcal and viral pharyngitis in children. Post-treatment observations showed an increase in potentially harmful bacteria and a decrease in beneficial bacteria in the oral cavity at the family level. Overall differences between the abundance of gut and oral microbiota were observed at the family level before and after treatment. The abundance of almost all microbiota groups was higher in FA than in SA, except for Streptococcus, Lachnospiraceae, and Prevotellaceae. After treatment, an overall increasing trend in the abundance of gut microbiota could be observed. However, the overall abundance of oral microbiota decreased, and the relative ranking of the abundance size of oral and gut microbiota was consistent before and after treatment.

[0090] Comparison of species abundance between oral and intestinal microbiota in patients with esophageal cancer before and after treatment ( Figure 3 d) At the species level, no significant changes were observed in any detectable microbiota in FA. However, SA showed a general decrease in the microbiota, including species of Bacteroidetes vulgatus, Bifidobacteriaceae, Lactobacillaceae, and Streptococcus pneumoniae, suggesting that the treatment may have a more direct effect on the oral microbiota.

[0091] At the species level, the relative abundance differences of oral and intestinal microbiota were the same before and after treatment. In contrast to observations at the family level, the abundance of each bacterium was higher in the oral cavity than in the intestine. After chemoradiotherapy, the abundance of oral microbiota showed an overall downward trend, while the overall abundance of intestinal microbiota remained relatively stable. The order of abundance of oral and intestinal microbiota did not change before and after treatment.

[0092] The abundance of different species in the oral cavity and intestine showed significant changes between SB, SA, FB, and FA. In SB, 12 differentiation groups were enriched, among which Leuconostocaceae was dominant and Pseudomycobacterium was the least prominent. In SA, 33 differentiation groups were enriched, among which Bacteroidetes and Bacteroidetes were the most abundant, and Uncariaceae NK3A20 was the least abundant. The FB6 differentiation group was enriched, with the highest abundance of Lactobacillus mucosa and the lowest abundance of Eubacteriaceae. Meanwhile, in FA, 4 highly diverse groups were enriched, among which Christensenellaminut was the most abundant and Leuconostoc tachybacterium ( Figure 3 e). Notably, Bacteroidetes has been implicated in mental health, particularly anxiety and other psychological disorders, through the microbiota-gut-brain axis.

[0093] At the species level, there were significant differences in the abundance of different species in the oral cavity and the intestine. In FB, 10 unique species were enriched, of which Bacteroides had the highest abundance. FA enriched 7 species, with Bacteroides fragilis having the highest abundance. SB enriched 9 different species, of which Actinomyces grevenitzi F0530 was the most abundant. SA enriched 33 differentiated species, of which Prevotella sp oral_taxon_299_str_F0039( Figure 3 f). The abundance of Parabacillus in FB was significantly reduced after treatment. In male mice, Parabacillus is a recognized probiotic that colonizes the human gastrointestinal tract and alleviates obesity and metabolic dysfunction by producing succinate and succinate. In addition, it may improve liver fibrosis by regulating intestinal bile acid metabolism and hepatocyte pyroptosis.

[0094] The most significant increase in abundance of Bacteroides fragilis in FA was noted, which has been previously mentioned as a harmful bacterium. Several bacterial groups were elevated in abundance in SA, including Eubacterium nodularis. Studies have shown that Eubacterium nodularis is the dominant strain in descending necrotizing mediastinitis (DNM), suggesting a potential link to the pathogenesis of DNM.

[0095] This application also explores the correlation between oral microbiota and intestinal microbiota. It can be observed that the diversity of oral and intestinal microbiota changes after treatment, and the β-diversity further differs. After chemotherapy and radiotherapy, harmful bacteria increase and beneficial bacteria decrease in oral and intestinal microbiota.

[0096] Figure 4 The relationship between oral microbiota and gut microbiota and clinical factors including sex, age, treatment, comorbidities, efficacy, and progression-free survival (PFS) is shown. The heat map shows the correlation between species-level oral microbiota and these clinical parameters ( Figure 4 a). A positive correlation can be seen between the enrichment of S. pneumoniae in the oral microbiota and complications. In contrast, a negative correlation was observed with treatment efficacy, probably due to the pathogenicity of S. pneumoniae to humans. In addition, it is the main cause of bacterial pneumonia in neutropenic cancer patients and meningitis in multiple myeloma patients.

[0097] Similarly, Capnocytophaga sp oral_taxon_863_str_F0517 was negatively correlated with efficacy. Studies have shown that carbonaceous phagocytosis is associated with complications (oral mucositis) of head and neck radiotherapy. Australian coral coral, pharyngeal streptococcus, and TM7 were highly significantly positively correlated with efficacy. Porphyromonas endodontica and Prevotella pallens ATCC 700821 were significantly negatively correlated with gender. Lactobacillus aerogenes, Bifidobacterium longum, and Bacteroides vulgaris were significantly positively correlated with age. Prevotella pallens ATCC 700821 and carbonaceous phagocytosis sp oral_taxon_863_str_F0517 were significantly negatively correlated with age. Muscicapsid and TM7_phylum_sp_oral_clone_cw040 were negatively correlated with treatment. At the species level, salivary microbiota was not significantly associated with PFS.

[0098] At the species level, the gut microbiota showed associations with clinical factors ( Figure 4b). Lactobacillus salivarius was enriched in fecal samples, positively correlated with treatment, and negatively correlated with gender. However, a study exploring the relationship between Helicobacter pylori infection and Lactobacillus species in the intestinal microbiota showed that in subjects without Helicobacter pylori, neither gender nor age affected the relative abundance of Lactobacillus in fecal samples (Iino C, Shimoyama T, Chinda D, Arai T, Chiba D, Nakaii S, Fukuda S. 2018. Infection of Helicobacter pylori and AtrophicGastritis Influence Lactobacillus in Gut Microbiota in a Japanese Population. Front Immunol 9: 712.). Psychrobacter and Bacteroides bacteria were negatively correlated with gender, while Ruminococcus masaiensis-P328 was strongly positively correlated with efficacy. The study found that the combined treatment of colorectal cancer with Ornithogalum polysaccharide and 5-FU can lead to the specific enrichment of Ruminococcus in the intestinal microbiota. This enrichment leads to increased levels of fecal short-chain fatty acids, improves the therapeutic effect, and reduces the adverse reactions caused by 5-FU (Yuan X, Xue J, Tan Y, Yang Q, Qin Z, Bao X, Li S, Pan L, Jiang Z, Wang Y, Lou Y, Jiang L, Du J. 2021. Albuca Bracteate Polysaccharides Synergistically Enhance the Anti-Tumor Efficacy of 5-Fluorouracil Against Colorectal Cancer by Modulating β-Catenin Signaling and Intestinal Flora. Front Pharmacol 12: 736627). Mucosal Lactobacillus in the intestinal microbiota was positively correlated with PFS. Bifidobacterium longum subspecies longum and Bacteroides ovatus CL02T12C04 were negatively correlated with treatment, while Parabacterium heteroschisis was significantly positively correlated with treatment. However, no intestinal microbiota associated with complications was observed at the species level.

[0099] This application explored the correlation between fecal and oral microbiota and clinical factors at the species level. The oral and intestinal microbiota of Pneumococcus, Lactobacillus salivarius, and Kakabacterium were negatively correlated with gender. Prevotella pallens ATCC 700821 and Pneumococcus were positively correlated with age. In contrast, Lactobacillus salivarius was negatively correlated with age. Lactobacillus salivarius and Parabacillus subtilis were significantly positively correlated with treatment, and Bifidobacterium longum was significantly negatively correlated with treatment. The long-chain bacillus subgroup was positively correlated with efficacy. Lactobacillus salivarius was positively correlated with PFS. At the species level, no fecal and oral microbiota showed an association with complications ( Figure 4 c).

[0100] This application also investigated the relationship between the microbiota and clinical factors in patients with esophageal cancer after chemoradiotherapy. Red arrows represent clinical factors, and the length of the arrows represents the strength of their association with the microbiota. The longer the red arrow, the stronger the correlation. It is worth noting that gender, age, treatment method, complications, and efficacy are significantly correlated with the patient's microbiota structure. Among these factors, age has the most significant relationship with bacterial colonization, followed by treatment method. PFS has the least correlation with the microbiota ( Figure 4 d).

[0101] Furthermore, the present application verifies the relationship between clinical factors and microbiota at the species level. Among all the factors considered, treatment mode has the highest correlation with microbiota, followed by gender, while efficacy has the lowest correlation with microbiota ( Figure 4 e). A bar graph was used to depict the correlation between microbiota and environmental factors at the species level. Specifically, treatment had the highest correlation with microbiota, followed by gender, while efficacy had the lowest correlation with microbiota ( Figure 4 f). Among all microbial groups, TM7_phylum_sp oral_clone_cw040 showed a strong positive correlation with efficacy, followed by Ruminococcus massiliense-P328 and Streptococcus angiopathy. It is worth noting that TM7 is expected to be a potential therapeutic bacterium to synergistically improve the efficacy of chemoradiotherapy for esophageal cancer.

[0102] Although the technical solutions of the present invention have been described and listed in detail, it should be understood that it is obvious to those skilled in the art to make modifications to the above embodiments or adopt equivalent alternatives. These modifications or improvements made without departing from the spirit of the present invention are within the scope of protection required by the present invention.

Claims

1. A marker microbiome associated with esophageal cancer radiotherapy and chemotherapy, characterized in that: The marker microbiota includes beneficial and harmful bacteria in the oral cavity and intestines; The beneficial bacteria include: Porites australis, Streptococcus pharyngeus, TM7, Lactobacillus gasseri, Ruminococcus, Bacteroides intestinalis, Lactobacillus salivarius, Psychrobacterium psychrophilum, Lactobacillus mucosa; The harmful bacteria include: Streptococcus pneumoniae, Capnocytophaga, Weissella, Bifidobacterium longum, and Bacteroides vulgaris; After chemoradiotherapy, the changes in the abundance of the above-mentioned marker microbiota in the oral cavity and intestine of patients were identified to determine the therapeutic efficacy and side effects of chemoradiotherapy for esophageal cancer: The marker microbial community is screened and identified by the following method: Step 1: Sample collection and DNA extraction: After the patient's pathological diagnosis, stool and saliva samples were collected and stored in sterile containers at -20°C using RNA later. 200 mg of microbial macro DNA was extracted from each sample using a trace sample DNA extraction kit; Step 2: 16S rRNA gene sequencing: A high-fidelity PCR mixture was used for PCR reaction. Before sequencing, the barcode-specific primers 515F-806R were used to amplify different 16S rRNA genes in the V3-V4 high-variability region. A DNA-free PCR sample protection kit was used to generate a sequence library from the purified PCR product. The quality of the library was determined using a fluorimeter and a bioanalyzer. Then, sequencing was performed on a sequencer to generate 250 bp double-end reads. Step 3: Bioinformatics analysis: assign the obtained paired-end reads to samples according to the barcode; truncate the barcode and primer sequences, and connect the truncated reads using FLASH; perform quality control on the original tags, compare the clean tags with the reference database to detect chimeric sequences; obtain valid tags after removing chimeric sequences; perform sequence analysis, 97% of the sequences are distributed on the same OTU, perform sequence screening for each OTU, annotate the classification data, perform multiple sequence alignment, analyze phylogenetic relationships and identify microbial communities.

Images